0%| | 0/10 [00:00<?, ?it/s] Python Never Ends Running

hello, Im trying to run this github code :
https://github.com/SamuelLiebana/4G3...m_rand_init.py

Code:

import numpy as np import matplotlib.pyplot as plt import random from bitstring import BitArray from scipy.stats import norm from tqdm import tqdm """ Implementation of Hopfield Network to calculate the simulated error probability for a network initialised at corrupted memory. The result is then compared to the values obtained from the analytical expression for the error probability.""" # CONSTANTS N = 100 # number of neurons in network M_an = np.array(range(2,1001)) # number of memories for analytical values of p_e # number of memories for simulated values of p_e M_sim = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40,45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000 ] # input noises (i.e. probability of flipping a bit # of original memory in initial state) input_noise = [0, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 1] # placeholder for simulated error probabilities error_probs = np.zeros([len(input_noise), len(M_sim)]) # SIMULATION AND ERROR PROBABILITY for noise in tqdm(range(len(input_noise))): # counting variable for indexing error_probs index = 0 # loop over number of memories for m in M_sim: n_errors=0 index += 1 # increase counting variable # for 50 different collections of memories for n in range(50): # initialise empty memory array mem_array = np.zeros([m, N]) # generate random memories (i.e. bit patterns) for i in range(m): mem = random.randint(0, 2**N - 1) mem_bin = BitArray(uint=mem, length=N) mem_array[i] = mem_bin # calculate weight matrix W = np.matmul(np.transpose(mem_array)-0.5, mem_array-0.5) np.fill_diagonal(W, 0) # choose initial memory states orig_mems = mem_array[np.random.choice(range(m), 50)] # average over chosen memory states for orig_mem in orig_mems: # average over 50 memory state corruptions for each # selected memory for i in range(50): # flip the bits according to the input noise mask = np.random.binomial(1, input_noise[noise], N) r = np.logical_xor(orig_mem, mask) # asynchronous update from Equation 1.1.2 r_new = np.matmul(W, r) # implementation of step function applied # to the input for the first neuron new_val = 0 if r_new[0]<0 else r[0] if r_new[0]==0 else 1 n_errors += 1 if new_val != orig_mem[0] else 0 # calculate probability as proportion of changes prop_errors = n_errors/(50*50*len(orig_mems)) # populate error probability array error_probs[noise, index-1] = prop_errors # PLOTTING # plot of simulated values fig = plt.figure() ax = fig.add_subplot(111) sim_error_0, = ax.plot(M_sim, error_probs[0], linestyle = '--', color = 'k') ax.plot(M_sim, error_probs[1], linestyle = '--', color = 'b') ax.plot(M_sim, error_probs[2], linestyle = '--', color = 'm') ax.plot(M_sim, error_probs[3], linestyle = '--', color = 'g') ax.plot(M_sim, error_probs[4], linestyle = '--', color = 'c') ax.plot(M_sim, error_probs[5], linestyle = '--', color = 'r') ax.plot(M_sim, error_probs[6], linestyle = '--', color = 'brown') ax.plot(M_sim, error_probs[7], linestyle = '--', color = 'olive') ax.plot(M_sim, error_probs[8], linestyle = '--', color = 'orange') ax.plot(M_sim, error_probs[9], linestyle = '--', color = 'darkgray') # calculate analytical values of the error probability p_e_0 = norm.cdf(-np.sqrt((N-1)/(2*(M_an-1)))) p_e_0_01 = norm.cdf((2*0.01-1)*np.sqrt((N-1)/(2*(M_an-1)))) p_e_0_02 = norm.cdf((2*0.02-1)*np.sqrt((N-1)/(2*(M_an-1)))) p_e_0_05 = norm.cdf((2*0.05-1)*np.sqrt((N-1)/(2*(M_an-1)))) p_e_0_1 = norm.cdf((2*0.1-1)*np.sqrt((N-1)/(2*(M_an-1)))) p_e_0_2 = norm.cdf((2*0.2-1)*np.sqrt((N-1)/(2*(M_an-1)))) p_e_0_5 = norm.cdf((2*0.5-1)*np.sqrt((N-1)/(2*(M_an-1)))) p_e_0_8 = norm.cdf((2*0.8-1)*np.sqrt((N-1)/(2*(M_an-1)))) p_e_0_9 = norm.cdf((2*0.9-1)*np.sqrt((N-1)/(2*(M_an-1)))) p_e_1 = norm.cdf((2-1)*np.sqrt((N-1)/(2*(M_an-1)))) # plot analytical values of the error probability analytical_error_0, = ax.plot(M_an, p_e_0, label = '$p_{noise} = 0$', color = 'k') ax.plot(M_an, p_e_0_01, label = '$p_{noise} = 0.01$', color = 'b') ax.plot(M_an, p_e_0_02, label = '$p_{noise} = 0.02$', color = 'm') ax.plot(M_an, p_e_0_05, label = '$p_{noise} = 0.05$', color = 'g') ax.plot(M_an, p_e_0_1, label = '$p_{noise} = 0.1$', color = 'c') ax.plot(M_an, p_e_0_2, label = '$p_{noise} = 0.2$', color = 'r') ax.plot(M_an, p_e_0_5, label = '$p_{noise} = 0.5$', color = 'brown') ax.plot(M_an, p_e_0_8, label = '$p_{noise} = 0.8$', color = 'olive') ax.plot(M_an, p_e_0_9, label = '$p_{noise} = 0.9$', color = 'orange') ax.plot(M_an, p_e_1, label = '$p_{noise} = 1.0$', color = 'darkgray') # settings for x and y labels and ticks and legend ax.set_xlabel('Number of Memories (M)', size = 15) ax.set_ylabel('Probability of Error ($p_e$)', size = 15) ax.tick_params(labelsize=15) leg1 = ax.legend(prop={'size': 15}) # ensuring that the x-axis is logarithmic plt.xscale('log') # add two legends (one for an/sim and one for input noise level) leg2 = ax.legend([sim_error_0, analytical_error_0],['Simulated','Analytical'], loc='upper left', prop={'size': 15}) ax.add_artist(leg1) plt.show()

-progress bar "0%| | 0/10 [00:00<?, ?it/s] " appears and stays at 0.
I believe that somethings wrong with the for loops but i dont know what.. Im running the code with Python 3.8.10 from idle or cmd panel